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href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="post-info"><h1 class="post-title">RAG的简单流程实现</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2025-07-02T01:59:46.000Z" title="发表于 2025-07-02 09:59:46">2025-07-02</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-07-02T02:28:47.219Z" title="更新于 2025-07-02 10:28:47">2025-07-02</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/">人工智能</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="RAG的简单流程实现"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><h1 id="RAG的简单流程实现"><a href="#RAG的简单流程实现" class="headerlink" title="RAG的简单流程实现"></a>RAG的简单流程实现</h1><h1 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h1><p>RAG的简单流程实现。</p>
<hr>
<h1 id="流程"><a href="#流程" class="headerlink" title="流程"></a>流程</h1><ul>
<li><p>准备知识库</p>
<p>规范格式</p>
</li>
<li><p>chunking分块&#x2F;段</p>
<p>通用分块；父子分块</p>
<p>分隔符、最大块长度、块重叠长度（连接上下文长度）、…</p>
</li>
<li><p>embedding嵌入</p>
<p>1.embedding嵌入模型</p>
<p>word2vec</p>
<p>经济索引（每块10个关键字搜）；高质量索引（embedding模型处理搜）</p>
<p>vector db</p>
</li>
<li><p>检索测试</p>
</li>
<li><p>查询同理，匹配相似度高&#x2F;向量距离近&#x2F;数据库相近的几条</p>
<p>2.rerank检索排序（多路）召回模型</p>
<p>向量搜索；全文&#x2F;倒排搜索；混合搜索（向量语义（索引）+关键词（索引））</p>
<p>分数阈值；topK</p>
</li>
</ul>
<hr>
<h1 id="缺点和优化"><a href="#缺点和优化" class="headerlink" title="缺点和优化"></a>缺点和优化</h1><p>缺点</p>
<ul>
<li>分块不准 </li>
<li>多句没有哪句特别相关</li>
</ul>
<p>优化</p>
<ul>
<li><p>graph rag知识图谱rag</p>
<p>图联系上下文，减少传统rag缺点</p>
</li>
</ul>
<hr>
<h1 id="代码实现"><a href="#代码实现" class="headerlink" title="代码实现"></a>代码实现</h1><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 1.准备知识库————————————————————</span></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">read_knowledge_base</span>() -&gt; <span class="built_in">str</span>:</span><br><span class="line">    <span class="keyword">with</span> <span class="built_in">open</span>(<span class="string">&quot;knowledge_base.md&quot;</span>, <span class="string">&quot;r&quot;</span>, encoding=<span class="string">&quot;utf-8&quot;</span>) <span class="keyword">as</span> f:</span><br><span class="line">        <span class="keyword">return</span> f.read()</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.chunking分块/段————————————————————</span></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">divide_chunks</span>() -&gt; <span class="built_in">list</span>[<span class="built_in">str</span>]:</span><br><span class="line">    text = read_knowledge_base()</span><br><span class="line">    tmp_chunks = text.split(<span class="string">&quot;\n\n&quot;</span>)</span><br><span class="line"></span><br><span class="line">    <span class="comment"># title不分块，和下一块合并</span></span><br><span class="line">    chunks = []</span><br><span class="line">    title = <span class="string">&quot;&quot;</span></span><br><span class="line">    <span class="keyword">for</span> c <span class="keyword">in</span> tmp_chunks:</span><br><span class="line">        <span class="keyword">if</span> c.startswith(<span class="string">&quot;#&quot;</span>):</span><br><span class="line">            title += <span class="string">f&quot;<span class="subst">&#123;c&#125;</span>\n&quot;</span>  <span class="comment"># 记录</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            chunks.append(<span class="string">f&quot;<span class="subst">&#123;title&#125;</span><span class="subst">&#123;c&#125;</span>&quot;</span>)  <span class="comment"># 合并</span></span><br><span class="line">            title = <span class="string">&quot;&quot;</span>  <span class="comment"># 重置</span></span><br><span class="line"></span><br><span class="line">    <span class="keyword">return</span> chunks</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># if __name__ == &quot;__main__&quot;:</span></span><br><span class="line"><span class="comment">#     chunks = divide_chunks()</span></span><br><span class="line"><span class="comment">#     for c in chunks:</span></span><br><span class="line"><span class="comment">#         print(c)</span></span><br><span class="line"><span class="comment">#         print(&quot;---------------&quot;)</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 3.embedding嵌入————————————————————</span></span><br><span class="line"><span class="keyword">from</span> google <span class="keyword">import</span> genai</span><br><span class="line"></span><br><span class="line"><span class="comment"># 获取api密钥：https://aistudio.google.com/apikey（需要魔法）</span></span><br><span class="line"><span class="comment"># 作者已删除该密钥</span></span><br><span class="line">genai_client = genai.Client(api_key=<span class="string">&quot;AIzaSyDOai6QVu_r_fMRbFeLv5-rSyljpqpAhWc&quot;</span>)</span><br><span class="line">EMBED_MODEL = <span class="string">&quot;gemini-embedding-exp-03-07&quot;</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">embed</span>(<span class="params">text: <span class="built_in">str</span>, store: <span class="built_in">bool</span></span>) -&gt; <span class="built_in">list</span>[<span class="built_in">float</span>]:  <span class="comment"># 模型分为存储和查询模式</span></span><br><span class="line">    result = genai_client.models.embed_content(</span><br><span class="line">        model=EMBED_MODEL,</span><br><span class="line">        contents=text,</span><br><span class="line">        config=&#123;<span class="string">&quot;task_type&quot;</span>: <span class="string">&quot;RETRIEVAL_DOCUMENT&quot;</span> <span class="keyword">if</span> store <span class="keyword">else</span> <span class="string">&quot;RETRIEVAL_QUERY&quot;</span>&#125;,</span><br><span class="line">    )</span><br><span class="line"></span><br><span class="line">    <span class="keyword">assert</span> result.embeddings</span><br><span class="line">    <span class="keyword">assert</span> result.embeddings[<span class="number">0</span>].values</span><br><span class="line">    <span class="keyword">return</span> result.embeddings[<span class="number">0</span>].values</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># if __name__ == &quot;__main__&quot;:</span></span><br><span class="line"><span class="comment">#     chunks = divide_chunks()</span></span><br><span class="line"><span class="comment">#     print(embed(chunks[0], store=True))</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> chromadb</span><br><span class="line"></span><br><span class="line">chromadb_client = chromadb.PersistentClient(<span class="string">&quot;./chroma.db&quot;</span>)  <span class="comment"># 库</span></span><br><span class="line">chromadb_collection = chromadb_client.get_or_create_collection(<span class="string">&quot;linghuchong&quot;</span>)  <span class="comment"># 表</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">store_db</span>() -&gt; <span class="literal">None</span>:</span><br><span class="line">    chunks = divide_chunks()</span><br><span class="line"></span><br><span class="line">    <span class="keyword">for</span> idx, c <span class="keyword">in</span> <span class="built_in">enumerate</span>(chunks):</span><br><span class="line">        <span class="built_in">print</span>(<span class="string">f&quot;Process: <span class="subst">&#123;c&#125;</span>&quot;</span>)</span><br><span class="line">        vec = embed(c, store=<span class="literal">True</span>)</span><br><span class="line">        chromadb_collection.upsert(ids=<span class="built_in">str</span>(idx), documents=c, embeddings=vec)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># if __name__ == &quot;__main__&quot;:</span></span><br><span class="line"><span class="comment">#     store_db()  # 可能会因为网络失败</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4.检索测试</span></span><br><span class="line"><span class="comment"># 5.查询同理，匹配相似度高/向量距离近/数据库相近的几条</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">query_db</span>(<span class="params">question: <span class="built_in">str</span></span>) -&gt; <span class="built_in">list</span>[<span class="built_in">str</span>]:</span><br><span class="line">    vec = embed(question, store=<span class="literal">False</span>)</span><br><span class="line">    result = chromadb_collection.query(query_embeddings=vec, n_results=<span class="number">5</span>)</span><br><span class="line"></span><br><span class="line">    <span class="keyword">assert</span> result[<span class="string">&quot;documents&quot;</span>]</span><br><span class="line">    <span class="keyword">return</span> result[<span class="string">&quot;documents&quot;</span>][<span class="number">0</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">LLM_MODEL = <span class="string">&quot;gemini-2.5-flash-preview-05-20&quot;</span></span><br><span class="line"><span class="keyword">if</span> __name__ == <span class="string">&quot;__main__&quot;</span>:</span><br><span class="line">    <span class="comment"># store_db()</span></span><br><span class="line"></span><br><span class="line">    question = <span class="string">&quot;令狐冲领悟了什么魔法？&quot;</span></span><br><span class="line">    context = query_db(question)</span><br><span class="line"></span><br><span class="line">    system_prompt = <span class="string">&quot;You are clever\n&quot;</span></span><br><span class="line">    user_prompt = <span class="string">&quot;Please answer user&#x27;s question according to context\n&quot;</span></span><br><span class="line">    user_prompt += <span class="string">f&quot;Question: <span class="subst">&#123;question&#125;</span>\n&quot;</span></span><br><span class="line">    user_prompt += <span class="string">&quot;Context:\n&quot;</span></span><br><span class="line">    <span class="keyword">for</span> c <span class="keyword">in</span> context:</span><br><span class="line">        user_prompt += <span class="string">f&quot;<span class="subst">&#123;c&#125;</span>\n&quot;</span></span><br><span class="line">        user_prompt += <span class="string">&quot;-------------\n&quot;</span></span><br><span class="line">    prompt = system_prompt + user_prompt</span><br><span class="line"></span><br><span class="line">    result = genai_client.models.generate_content(model=LLM_MODEL, contents=prompt)</span><br><span class="line">    <span class="built_in">print</span>(result)</span><br><span class="line">    <span class="comment"># 没有继续提取answer了</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>

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<h1 id="结果"><a href="#结果" class="headerlink" title="结果"></a>结果</h1><p>divide_chunk()</p>
<p><img src= "" data-lazy-src="/../img/simlpe_rag/divide_chunk().png" alt="divide_chunk()"></p>
<p>embed()</p>
<p><img src= "" data-lazy-src="/../img/simlpe_rag/embed().png" alt="embed()"></p>
<p>store_db()</p>
<p><img src= "" data-lazy-src="/../img/simlpe_rag/store_db().png" alt="store_db()"></p>
<p>query()</p>
<p><img src= "" data-lazy-src="/../img/simlpe_rag/query().png" alt="query()"></p>
<hr>
<h1 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h1><ul>
<li><a target="_blank" rel="noopener" href="https://www.bilibili.com/video/BV19RJhzyEWN?spm_id_from=333.788.videopod.sections&vd_source=9ac1c0a6d496218112b60d49bc768cd7">这就是RAG 一看就懂的个人知识库架构_哔哩哔哩_bilibili</a></li>
<li><a target="_blank" rel="noopener" href="https://www.bilibili.com/video/BV1XzQqYaEBP/?spm_id_from=333.788.player.switch&vd_source=9ac1c0a6d496218112b60d49bc768cd7&p=8">08-个人知识库资料存储和检索的流程_哔哩哔哩_bilibili</a></li>
<li><a target="_blank" rel="noopener" href="https://www.bilibili.com/video/BV1zoKuzoENM?spm_id_from=333.788.videopod.sections&vd_source=9ac1c0a6d496218112b60d49bc768cd7">AI知识图谱 GraphRAG 是怎么回事？_哔哩哔哩_bilibili</a></li>
<li><a target="_blank" rel="noopener" href="https://www.bilibili.com/video/BV168j7zCE6D/?spm_id_from=333.788.videopod.sections&vd_source=9ac1c0a6d496218112b60d49bc768cd7">从零写AI RAG 个人知识库_哔哩哔哩_bilibili</a></li>
</ul>
<hr>
<h1 id="作者的话"><a href="#作者的话" class="headerlink" title="作者的话"></a>作者的话</h1><ul>
<li><strong>感谢参考资料的作者&#x2F;博主</strong></li>
<li>作者：夜悊</li>
<li>版权所有，转载请注明出处，谢谢~</li>
<li><strong>如果文章对你有帮助，请点个赞或加个粉丝吧，你的支持就是作者的动力~</strong></li>
<li>文章在描述时有疑惑的地方，请留言，定会一一耐心讨论、解答</li>
<li>文章在认识上有错误的地方, 敬请批评指正</li>
<li>望读者们都能有所收获</li>
</ul>
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</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta"><i class="fas fa-circle-user fa-fw"></i>文章作者: </span><span class="post-copyright-info"><a href="http://example.com">夜悊</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta"><i class="fas fa-square-arrow-up-right fa-fw"></i>文章链接: </span><span class="post-copyright-info"><a href="http://example.com/2025/07/02/RAG%E7%9A%84%E7%AE%80%E5%8D%95%E6%B5%81%E7%A8%8B%E5%AE%9E%E7%8E%B0/">http://example.com/2025/07/02/RAG的简单流程实现/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta"><i class="fas fa-circle-exclamation fa-fw"></i>版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="http://example.com" target="_blank">夜悊的技术小宅</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"><a class="post-meta__tags" href="/tags/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/">人工智能</a></div><div class="post_share"><div class="social-share" data-image="/img/cover16.png" data-sites="qq,wechat,weibo"></div><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/css/share.min.css" media="print" onload="this.media='all'"><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/sharejs/dist/js/social-share.min.js" defer></script></div></div><nav class="pagination-post" id="pagination"><div class="prev-post pull-full"><a href="/2025/07/01/%E6%8F%90%E7%A4%BA%E8%AF%8D%E5%B7%A5%E7%A8%8B%E7%9A%84%E5%9F%BA%E7%A1%80%E6%A6%82%E5%BF%B5/" title="提示词（工程）的基础概念"><img class="cover" src= "" data-lazy-src="/img/cover4.png" onerror="onerror=null;src='/img/404.jpg'" alt="cover of previous post"><div class="pagination-info"><div class="label">上一篇</div><div class="prev_info">提示词（工程）的基础概念</div></div></a></div></nav><div class="relatedPosts"><div class="headline"><i class="fas fa-thumbs-up fa-fw"></i><span>相关推荐</span></div><div class="relatedPosts-list"><div><a href="/2025/07/01/%E6%8F%90%E7%A4%BA%E8%AF%8D%E5%B7%A5%E7%A8%8B%E7%9A%84%E5%9F%BA%E7%A1%80%E6%A6%82%E5%BF%B5/" title="提示词（工程）的基础概念"><img class="cover" src= "" data-lazy-src="/img/cover4.png" alt="cover"><div class="content is-center"><div class="date"><i class="far fa-calendar-alt fa-fw"></i> 2025-07-01</div><div class="title">提示词（工程）的基础概念</div></div></a></div></div></div></div><div class="aside-content" id="aside-content"><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#RAG%E7%9A%84%E7%AE%80%E5%8D%95%E6%B5%81%E7%A8%8B%E5%AE%9E%E7%8E%B0"><span class="toc-number">1.</span> <span class="toc-text">RAG的简单流程实现</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%89%8D%E8%A8%80"><span class="toc-number">2.</span> <span class="toc-text">前言</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E6%B5%81%E7%A8%8B"><span class="toc-number">3.</span> <span class="toc-text">流程</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E7%BC%BA%E7%82%B9%E5%92%8C%E4%BC%98%E5%8C%96"><span class="toc-number">4.</span> <span class="toc-text">缺点和优化</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0"><span class="toc-number">5.</span> <span class="toc-text">代码实现</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E7%BB%93%E6%9E%9C"><span class="toc-number">6.</span> <span class="toc-text">结果</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99"><span class="toc-number">7.</span> <span class="toc-text">参考资料</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E4%BD%9C%E8%80%85%E7%9A%84%E8%AF%9D"><span class="toc-number">8.</span> <span class="toc-text">作者的话</span></a></li></ol></div></div></div></div></main><footer id="footer" style="background-image: url('/img/cover16.png')"><div id="footer-wrap"><div class="copyright">&copy;2023 - 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